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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
11:52

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Published on: August 4, 2016

Sensitive gene fusion detection using ambiguously mapping RNA-Seq read pairs.

Marcus Kinsella1, Olivier Harismendy, Masakazu Nakano

  • 1Bioinformatics and Systems Biology Program, Moores UCSD Cancer Center, Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA. mckinsel@ucsd.edu

Bioinformatics (Oxford, England)
|February 19, 2011
PubMed
Summary

This study introduces a new method to detect gene fusions using paired-end sequencing data, even with ambiguous read mappings. The approach effectively identifies fusion transcripts without needing unique mappings or extra sequencing, improving fusion detection accuracy.

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Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology
  • Cancer Research

Background:

  • Whole transcriptome sequencing (WTS) with paired-end reads is crucial for identifying fusion transcripts.
  • Transcriptome repetitiveness leads to multiple high-quality mappings for many reads, complicating fusion detection.
  • Existing methods often ignore ambiguously mapping reads or require additional single reads, potentially missing up to 30% of fusions.

Purpose of the Study:

  • To develop a novel method for detecting fusion transcripts using paired-end reads, specifically addressing challenges posed by ambiguous mappings.
  • To enable the utilization of ambiguously mapping reads in fusion transcript identification without compromising accuracy or requiring additional sequencing.

Main Methods:

  • Developed a computational method to analyze paired-end reads with multiple high-quality mapping locations.
  • The method does not require unique read mappings or supplementary single-read data.
  • Validated using simulated datasets and real-world data from tumors and cell lines.

Main Results:

  • The proposed method successfully identifies fusion transcripts from ambiguously mapping read pairs.
  • Demonstrated the ability to detect fusions without introducing a significant number of spurious results.
  • The approach effectively leverages data previously discarded or ignored by other methods.

Conclusions:

  • This new method enhances the detection of fusion transcripts by effectively utilizing paired-end reads with ambiguous mappings.
  • It offers a more comprehensive approach to fusion discovery, reducing data loss and improving sensitivity.
  • The C++ and Python implementation is publicly available, facilitating its adoption in research.